Episode 29 — Model Threats Effectively Using STRIDE and PASTA

Threat modeling is one of the most powerful analytical tools in the CSSLP toolkit, and structured methods like STRIDE and PASTA help you apply it consistently. This episode explains how to define the scope of a threat model by identifying assets, actors, trust boundaries, and critical data flows. STRIDE is broken down into its categories of spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege, with practical definitions that map directly to software behavior. PASTA is presented as a multi-stage process that starts with objectives and application decomposition and moves through threat enumeration and vulnerability analysis. You will hear how both methods rely on clear diagrams and shared assumptions, making it easier for teams to reason about risk.

Using these models to drive decisions requires moving from lists of threats to prioritized actions. Detailed examples walk through applying STRIDE to each element of a data flow diagram, capturing plausible threats, and then evaluating their impact and likelihood using calibrated scales. PASTA-informed scenarios show how intelligence about attacker capabilities, recent exploits, and industry campaigns feeds into the assessment and helps avoid purely theoretical concerns. You will learn how to connect threats to specific controls, requirements, and test cases, creating a lineage that supports traceability and auditability. Exam-aligned practice comes from recognizing when a question describes an incomplete or shallow threat modeling exercise and selecting responses that add structure, validate assumptions, and turn findings into concrete backlog items with acceptance criteria. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.